Welcome to our guide on synthesizing person images using the Denoising Diffusion Model (PIDM). Whether you’re a novice or an experienced developer, we’ve made it user-friendly to help you navigate through the process. This guide will walk you through the steps to set up your environment, prepare your dataset, and run inference with the model.

Step 1: Setup Your Environment

We’ll start by installing the necessary packages in a conda virtual environment. Here’s how you can set it up:

bash
# 1. Create a conda virtual environment.
conda create -n PIDM python=3.7
conda activate PIDM
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

# 2. Clone the Repo and Install dependencies
git clone https://github.com/ankanbhunia/PIDM
pip install -r requirements.txt

Step 2: Preparing Your Dataset

Now, let’s gather and prepare the dataset. For this example, we’ll be using the DeepFashion dataset.

• Download img_highres.zip from the In-shop Clothes Retrieval Benchmark.
• Unzip the file and obtain a folder called img and place it under the .dataset/deepfashion directory.
• Next, download the train/test pairs and the keypoints pose.
• Once downloaded, place them in the corresponding directory.

Step 3: Training the Model

Our model supports multi-GPU training, which speeds up the training process significantly. To train, run the following command:

bash
python -m torch.distributed.launch --nproc_per_node=8 --master_port 48949 train.py --dataset_path .dataset/deepfashion --batch_size 8 --exp_name pidm_deepfashion

Training typically takes 5 days with 8 A100 GPUs; however, it shows significant results after just 200 epochs.

Step 4: Running Inference

After training, you can perform inference using our pretrained model. Download it from here and place it in the checkpoints folder. You can control pose and appearance with the following scripts:

python
from predict import Predictor

# For pose control
obj = Predictor()
obj.predict_pose(image=PATH_OF_SOURCE_IMAGE, sample_algorithm='ddim', num_poses=4, nsteps=50)

# For appearance control
src = PATH_OF_SOURCE_IMAGE
ref_img = PATH_OF_REF_IMAGE
ref_mask = PATH_OF_REF_MASK
ref_pose = PATH_OF_REF_POSE
obj.predict_appearance(image=src, ref_img=ref_img, ref_mask=ref_mask, ref_pose=ref_pose, sample_algorithm='ddim', nsteps=50)

The output will be saved as output.png.

Troubleshooting

If you face any issues during installation or while running the model, consider the following tips:

• Ensure that you are using the correct version of Python and that all dependencies are properly installed.
• Double-check the paths provided for the datasets and models.
• If you encounter any CUDA-related errors, verify that your GPU supports the required CUDA version.

For further assistance and resources, stay connected with **fxis.ai**.

Conclusion

At **fxis.ai**, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.